Lifelong person re-identification (LReID) exhibits a contradictory relationship between intra-domain discrimination and inter-domain gaps when learning from continuous data. Intra-domain discrimination focuses on individual nuances (e.g. clothing type, accessories, etc.), while inter-domain gaps emphasize domain consistency. Achieving a trade-off between maximizing intra-domain discrimination and minimizing inter-domain gaps is a crucial challenge for improving LReID performance. Most existing methods aim to reduce inter-domain gaps through knowledge distillation to maintain domain consistency. However, they often ignore intra-domain discrimination. To address this challenge, we propose a novel domain consistency representation learning (DCR) model that explores global and attribute-wise representations as a bridge to balance intra-domain discrimination and inter-domain gaps. At the intra-domain level, we explore the complementary relationship between global and attribute-wise representations to improve discrimination among similar identities. Excessive learning intra-domain discrimination can lead to catastrophic forgetting. We further develop an attribute-oriented anti-forgetting (AF) strategy that explores attribute-wise representations to enhance inter-domain consistency, and propose a knowledge consolidation (KC) strategy to facilitate knowledge transfer. Extensive experiments show that our DCR model achieves superior performance compared to state-of-the-art LReID methods. Our code will be available soon.

翻译：终身行人重识别（LReID）在学习连续数据时，域内判别性与域间差异之间存在矛盾关系。域内判别性关注个体细微差异（如服装类型、配饰等），而域间差异则强调领域一致性。在最大化域内判别性与最小化域间差异之间取得平衡，是提升LReID性能的关键挑战。现有方法大多通过知识蒸馏来减小域间差异以维持领域一致性，但往往忽略了域内判别性。为解决这一挑战，我们提出了一种新颖的领域一致性表征学习（DCR）模型，该模型探索全局表征与属性级表征作为桥梁，以平衡域内判别性与域间差异。在域内层面，我们探索全局表征与属性级表征之间的互补关系，以提高相似身份间的判别能力。过度学习域内判别性可能导致灾难性遗忘。我们进一步开发了一种面向属性的抗遗忘（AF）策略，通过探索属性级表征来增强域间一致性，并提出一种知识巩固（KC）策略以促进知识迁移。大量实验表明，与最先进的LReID方法相比，我们的DCR模型实现了更优的性能。我们的代码将很快公开。